code: def train_model(model, optimizer, train_loader):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(
              DEVICE
          )
          optimizer.zero_grad()
          F.nll_loss(model(data), target).backward()
          optimizer.step()
  
file_path: optuna/tutorial/20_recipes/002_multi_objective.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from fvcore.nn import FlopCountAnalysis
  import optuna
label_desc: Use image node_id 2 for calling a global function with example usage: train_model(model, optimizer, train_loader) without return types